Numerous examples of behavior contradict the predictions
of the standard rational choice model. People often fail to ignore
sunk costs. They play tennis indoors when, by their
own account, they would prefer to play outside. They behave
differently when they lose a ticket than when they lose
an equivalent amount of cash. Psychologists argue that such
behavior is the result of limitations in human cognitive capacity.
People use mental accounting systems that reduce the
complexity of their decisions, sometimes at the expense of
consistency with the axioms of rational choice.
An important class of departures from rational choice appears
to result from the asymmetric value function described
by Kahneman and Tversky. In contrast to the rational choice
model, which uses a utility function defined on total wealth,
Kahneman's and Tversky's descriptive theory uses a value
function defined over changes in wealth. Unlike the traditional
model, it gives losses much heavier decision weight
than gains. This feature makes decisions extremely sensitive
to how alternatives are framed. For example, if a loss is
combined with a slightly larger gain, the net effect typically
receives a positive evaluation, as it would under the rational
choice model. But Kahneman and Tversky suggest that
when gains and losses occur as discrete events, people tend
to evaluate their effects separately, in which case the impact
of the loss tends to outweigh that of the larger gain. A loss
combined with a slightly larger gain produces a positive
effect, whereas taken separately their net effect is negative.
Another source of suboptimal decisions is failure to anticipate
how we will adapt to different consumption experiences
over time. In choosing between two goods, people
tend to favor the alternative that provides greater satisfaction
at the moment of decision. Evidence suggests, however,
that the satisfaction provided by some goods and activities
tends to decay quickly over time, whereas for others it decays
less quickly or even increases. The upshot is a tendency
to spend too much on goods and activities in the former category,
and too little on those in the latter.
Decisions under uncertainty also often violate the prescriptions
of the expected utility model. Here, too, the asymmetric value
function provides a consistent description of several important
patterns. People tend to be risk averse in the domain of gains
but risk seeking in the domain of losses. The result is that subtle
differences in the framing of the problem can shift the mental
reference point used for reckoning gains and losses, which,
in turn, can produce radically different patterns of choice.
Another important departure from rational choice occurs in
the heuristics, or rules of thumb, people use to make
estimates of important decision factors. The availability
heuristic says that one way people estimate the frequency of
a given class of events is by the ease with which they can recall
relevant examples. This leads to predictable biases because
actual frequency is not the only factor that governs
how easy it is to recall examples. People tend to overestimate
the frequency of vivid or salient events, and of other events
that are especially easy to retrieve from memory.
Another important heuristic is representativeness. People estimate
the likelihood that an item belongs to a given class by
how representative it is of that class. We saw that this often
leads to substantial bias because representativeness is only
one of many factors that govern this likelihood. Shyness may
indeed be a trait representative of librarians, but because
there are so many more salespeople than librarians, it is
much more likely that a randomly chosen shy person is a
salesperson than a librarian.
Anchoring and adjustment is a third heuristic that often
leads to biased estimates of important decision factors. This
heuristic says that people often make numerical estimates by
first picking a convenient (but sometimes irrelevant) anchor
and then adjusting from it (usually insufficiently) on the basis
of other potentially relevant information. This procedure
often causes people to underestimate the failure rate of projects
with many steps. Such a project fails if any one of its essential
elements fails, which means that even if the failure
rate of each element is extremely low, a project with many
elements is nonetheless very likely to fail. Because people
tend to anchor on the failure rate for the typical step, and
adjust insufficiently from it, they often grossly overestimate
the likelihood of success. This may help explain the naive
optimism of people who start new businesses.
Another departure from rational choice traces to the psychophysics
of perception. Psychologists have discovered that
the barely perceptible change in any stimulus is proportional
to its initial level. This seems to hold true as well when the
stimulus in question is the price of a good or service. People
think nothing of driving across town to save $5 on a $25 radio,
but would never dream of doing so to save $5 on a
$1000 TV set.
Departures from rational choice may also occur because
people simply have difficulty choosing between alternatives
that are hard to compare. The rational choice model
assumes that we have complete preference orderings, but
in practice, it often seems to require a great deal of effort
for us to decide how we feel about even very simple
alternatives.
Finally, departures from rational choice may occur because
people lack sufficient willpower to carry out plans they believe
to be in their own interests. In such instances, people may try
to place tempting, but inferior, alternatives out of reach.
Behavioral models of choice often do a much better job of
predicting actual decisions than the rational choice model.
It is important to remember, however, that the behavioral
models claim no normative significance. That is, the mere
fact that they predict, for example, that people often do ignore
sunk costs should not be taken to mean that people
should ignore them. The rational choice model says we can
make better decisions by ignoring sunk costs, and most
people, on reflection, strongly agree. In this respect,
behavioral models of choice are an important tool for
helping us avoid common pitfalls in decision making.
